Apr 27, | Computer Science
Read the case study titled, “Why Does Cryptographic Software Fail? A Case Study and Open Problems,” located in Week 6 of the course shell (original article located at http://people.csail.mit.edu/nickolai/papers/lazar-cryptobugs.pdf). Use the Internet to research The Office of Personnel Management (OPM) Data Breach. Also, research the results that multiple organizations have experienced when they have implemented cryptographic software.
Write a three to four (3-4) page paper in which you:
Examine two (2) major mistakes The Office of Personnel Management (OPM) made with cryptographic software. Recommend two (2) actions that companies can take in order to avoid these common mistakes and vulnerabilities with cryptographic software
Briefly describe The Office of Personnel Management (OPM) and its experience with cryptographic software. Analyze the company’s actions (or lack thereof) following the mistake and / or vulnerability
Provide your opinion on The Office of Personnel Management (OPM) actions or plans to correct the mistake and / or vulnerability to avoid it from reoccurring. If The Office of Personnel Management (OPM) has not corrected or does not have a plan to correct the mistake and / or vulnerability, suggest one (1) action that it should take in order to correct the mistake and / or vulnerability. Provide a rationale for your response
Examine the most common results that The Office of Personnel Management (OPM) have experienced when they implemented cryptographic software. Speculate two (2) reasons why organizations see such results. Provide a rationale for your response
Use at least three (3) quality references in this assignment
Note: Wikipedia and similar Websites do not qualify as quality resources.
Your assignment must follow these formatting requirements:
Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions
Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length
The specific course learning outcomes associated with this assignment are:
Examine the challenges of computer security, including different threats and types of attacks
Describe computational security and symmetric encryption techniques and adoption to modern organizations
Use technology and information resources to research issues in cryptography
Write clearly and concisely about cryptography using proper writing mechanics and technical style conventions
Apr 27, | Computer Science
Read the attached articles. There are a lot of selection guidelines in the technology overview article, prioritize them and explain your thought process. In the crowdsourcing artilce, explains how malicious conduct can distort social media analytics, explain the distortions and the impact on business decision.
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G00260476
How Crowdsourcing Can Reduce the Reliability
of Social Media Analytics
Published: 19 February 2014
Analyst(s): Alexander Linden, Jenny Sussin
Social media sites can provide valuable information about customers’ views
and needs more quickly than traditional forms of feedback. But analytics
leaders need to recognize that malicious crowdsourcing can distort the
results of social media analytics.
Impacts
■
Detractors can use crowdsourcing to post malicious messages, which makes analytics leaders
question the value of social media analytics.
■
Intentional detractions are hard for analytics leaders to identify, and poor decision making can
result.
Recommendations
Analytics leaders must:
■
Recognize that the results of social media analytics may not always be reliable.
■
Ensure that your solution provider understands these issues well and can address them via
contributor analysis or can filter analysis by channel source.
■
Employ multichannel analysis and look for consistency in feedback across channels.
■
Keep records of malicious detraction, whether identified by communities or automated systems.
Analysis
Enterprises can amass enormous amounts of valuable information from the Internet. Customers and
prospects routinely use social media sites to air their views, wants and needs. As an analytics
leader, you can often identify quality deficiencies and customer desires far earlier by using social
media analytics (SMA) than by using traditional channels. But certain types of information warfare
(see Note 1) may reduce the reliability of SMA results. For example, information warfare could make
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weak signals (see Note 2) harder to detect when SMA is based on the views of only a few dozen or
several hundred customers.
It’s easy for malicious contributors, be they trolls or competitors, to distort perceptions on social
media sites via crowdsourcing. They do this mainly to create a false picture of reality to harm an
opponent or benefit themselves. If the information that you analyze is distorted by malicious
crowdsourcing, the results of SMA won’t be accurate. You must, therefore, treat the results of SMA
with caution (see Figure 1).
Figure 1. Impacts and Top Recommendations for Analytics Leaders
Impacts
Top Recommendations
• Recognize that the results of SMA may not
Detractors can use crowdsourcing to
post malicious messages, which makes
analytics leaders question the value of
SMA.
always be reliable.
• Ensure that your solution provider
understands these issues and addresses
them via contributor analysis or filters by
channel source.
• Employ multichannel analysis and look for
Intentional detractions are hard for
analytics leaders to identify, and poor
decision making can result.
consistency in feedback across channels.
• Keep records of malicious detraction,
whether identified by communities or
automated systems.
SMA = social media analytics
Source: Gartner (February 2014)
The three main ways in which unscrupulous companies use crowdsourcing to distort reality are by:
■
Faking positive or negative reviews
■
Triggering irrational “herd behavior”
■
Distorting opinion
Faking positive or negative reviews: Companies can use crowdsourcing to obtain positive
reviews of their own products or services (see “The Consequences of Fake Fans, ‘Likes’ and
Reviews on Social Networks” [Note: This document has been archived; some of its content may not
reflect current conditions]). In 2013, New York regulators announced that they had conducted a
year-long investigation into the practice. As a result, 19 companies based in New York agreed to
stop procuring positive reviews and to pay a total of $350,000 in penalties. Dentists, lawyers and an
1
ultrasound clinic were among those who had paid for fake reviews. Likewise, companies can
contract Internet users to say bad things about the products or services of competitors (see “Solve
the Problem of Fake Online Reviews or Lose Credibility With Consumers”).
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But competitors aren’t the only ones who have a motive for posting misleading feedback.
Sometimes an organization’s employees may have a reason for doing so too. For example, vehicle
manufacturers examine social media feedback to determine the success of their dealerships. As
reviews affect the ratings of individual dealerships and sales and service teams, employees could
post negative feedback about other dealerships and positive feedback about their own.
Procuring or posting fake reviews contravenes corporate policies in many companies. Amazon
attempted to prevent it by putting “Amazon Verified Purchase” endorsements next to reviews. But
analytics leaders still draw aggregate reviews — one of the most frequently used filter criteria —
from nonverified purchases as well.
2
Triggering irrational “herd behavior”: Crowds can behave in irrational ways, often prompted by
3
the planting of information or misinformation. Research has shown that social media is not exempt
4,5,6,7
from such herd behavior and can be easily manipulated.
Distorting opinion: Companies can use pre-existing public conceptions or misconceptions to
perpetuate a message for their own gain or to distort a competitor’s view of reality. They can do this
by (indirectly) tasking microworkers to rephrase what genuine users have been saying about
products and companies. It would be very hard for any SMA approach to differentiate between the
opinions posted by genuine contributors and those posted by microworkers. An unscrupulous
company could, for example, use crowdsourcing to amplify certain problems identified by a
competitor’s customers. This could severely distort the results that the competitor derives from
SMA. For example, the issue reported most often by genuine customers could appear in SMA
results to be the third most frequently mentioned issue. This could lead to poor decision making by
the duped company. Such activity is extremely difficult to detect.
Impacts and Recommendations
Detractors can use crowdsourcing to post malicious messages, which makes analytics
leaders question the value of SMA
Those wishing to post malicious messages can do so quite easily by using a form of crowdsourcing
8
— also referred to as microwork or piecework — via online marketplaces. Hundreds of
marketplaces exist, such as Amazon’s Mechanical Turk, Fiverr.com and TaskRabbit.com. The
anonymity afforded by the Internet enables users to post malicious messages with little difficulty.
Detractors are more likely to use some channels than others. For example, they are more likely to
use microblogging sites than real-identity-driven networking sites such as LinkedIn. Those wishing
to post malicious messages find it faster and easier to set up rogue accounts on microblogging
sites such as Twitter and Sina Weibo. Posts on microblogging sites are short, quick and numerous
— and readers often take them at face value. Readers also view them in the context of a
conversation stream, which enables malicious detractors to make an impact quickly.
Recommendations:
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■
Recognize that the results of SMA may not always be reliable.
■
Ensure that your solution provider understands these issues well and can address them via
contributor analysis or can filter analysis by channel source.
Intentional detractions are hard for analytics leaders to identify, and poor decision
making can result
Many companies present SMA as a way to save time and money on traditional market research.
They believe social media sites give customers the opportunity to talk about enterprises and their
products in a relaxed way using a medium with which they feel comfortable. Traditional market
research feels staged and artificial in comparison.
But an unscrupulous company could harm a competitor by intentionally creating one misleading
post after another on social media sites. Numerous false alarms could distract the competitor from
dealing with real problems. This has led to the perception that SMA is not reliable.
Many organizations deal with this by conducting traditional market research alongside SMA. They
recognize that the results can be misleading if they analyze feedback from either channel in
isolation. Combining the results gives them a more reliable picture.
If you work in a highly regulated industry, using SMA may not be worth the risk — many companies
in such industries have opted to use only traditional feedback channels, such as surveys.
Regulations may dictate that you must take action as a result of negative feedback. Pharmaceutical
companies, for example, are required to report any adverse effects of drugs. The success of a drug
could be severely hindered by acting on the analysis of intentionally misleading social media posts.
R&D cycles could also be affected.
Recommendations:
■
Employ multichannel analysis and look for consistency in feedback across channels.
■
Keep records of malicious detraction, whether identified by communities or automated systems.
■
Share your records of malicious detraction with industry consortia to help alert your peers.
■
Work with regulators if you operate in a highly regulated industry to determine what types of
social media content are subject to compliance processes.
■
Work with regulators to develop a way of vetting social media content, which may make the
introduction of a reporting process unnecessary.
Gartner Recommended Reading
Some documents may not be available as part of your current Gartner subscription.
“Technology Overview for Social Analytics for Public-Facing Social Media”
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“The Consequences of Fake Fans, ‘Likes’ and Reviews on Social Networks”
Evidence
This report is based on the authors’ experience of social analytics. The authors have also drawn on
information they gathered when discussing the subject with experts from a technology corporation,
a cloud computing company and a competitive intelligence think tank.
1
D. Streitfeld, “Give Yourself Five Stars? Online, It Might Cost You,” nytimes.com, 22 September
2013.
2
Stock Market Bubbles section of “Herd Behavior,” Wikipedia, as of 4 February 2014.
3
L. Muchnik, S. Aral, S. Taylor, “Social Influence Bias: A Randomized Experiment,” Science, 9
August 2013.
4
C. Johnson, “The Pitfalls of Crowdsourcing: Online Ratings Vulnerable to Bias,” Boston.com, 8
August 2013.
5
S. Cass, “Crowdsource Control,” IEEE Spectrum, 2 October 2013.
6
A. Seave, “New Study Shows How Social Influence Can Significantly Manipulate Online Ratings,”
Forbes, 12 September 2013.
7
P. Dizikes, “Views You Can Use? How Online Ratings Affect Your Judgment,” MITnews, 8 August
2013.
8
“Predicts 2012: Ramifications of the Transition to HTML5”
Note 1 Definition of Information Warfare
Wikipedia defines information warfare as a concept that involves “the use and management of
information technology in pursuit of a competitive advantage over an opponent. Information warfare
may involve collection of tactical information, assurance(s) that one’s own information is valid,
spreading of propaganda or disinformation to demoralize or manipulate.” Definition on wikipedia.org
on 11 February 2014.
Note 2 Definition of Weak Signals
“Weak signals are those ambiguous and controversial bits of information about the competitive
environment that are typically hidden among the ‘noise’ of the prevailing sense-making paradigm
and that gradually coalesce to form a pattern of intelligence that alerts sensitive leaders that it may
be time to change their game.” — Dr. P. Saul, “Seeing the Future in Weak Signals,” Journal of
Futures Studies, February 2006, Vol. 10, No. 3, pp. 93-102.
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G00273268
Technology Overview for Social Analytics
Applications
Published: 12 March 2015
Analyst(s): Jenny Sussin, Carol Rozwell, Rita L. Sallam
Social analytics applications address a wide array of use cases across an
organization. IT application leaders must understand the variability in social
analytics application functionality when undertaking the vendor selection
process.
Key Findings
■
Social analytics strategies and applications are the No. 1 inquiry topic among IT clients
concerned with public-facing social media.
■
Social analytics vendors largely market themselves the same way, making application
delineation and selection difficult.
■
The cost of ownership of a social analytics tool varies greatly, from about $18,000/year to high
six-figure deals, depending on the scope of work, degree of customization and pricing model.
Recommendations
IT application leaders:
■
Before beginning any exploration of social analytics technology solutions, clarify the problem
you are trying to solve or the opportunity you want to pursue.
■
Emphasize specific selection criteria, such as real-time analysis or content sources or
integration capabilities, based on the business objectives and use cases being addressed.
■
Identify the pricing model that best complements your social analytics initiative, and look for
vendor solutions that are most favorable for the work being done.
■
Conduct a POC to focus on functional fit and real business value rather than relying on
marketing claims and generic demonstrations.
Table of Contents
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What You Need to Know…………………………………………………………………………………………………………2
Analysis……………………………………………………………………………………………………………………………….. 3
Technology Description…………………………………………………………………………………………………….. 4
Uses……………………………………………………………………………………………………………………………….4
Benefits and Risks……………………………………………………………………………………………………………. 6
Technology Alternatives……………………………………………………………………………………………………..7
Social Media Monitoring………………………………………………………………………………………………. 7
Consulting Services…………………………………………………………………………………………………….. 7
Selection Guidelines…………………………………………………………………………………………………………. 7
1. Information Sources………………………………………………………………………………………………….8
2. Information Analysis………………………………………………………………………………………………….9
3. Metrics………………………………………………………………………………………………………………… 10
4. Advanced Analytics Capabilities………………………………………………………………………………..11
5. Reporting, Dashboards and Visualization…………………………………………………………………… 11
6. Routing, Notification and Alerting……………………………………………………………………………… 12
7. Scalability and Architecture Requirements…………………………………………………………………. 12
Price Performance…………………………………………………………………………………………………………..13
Technology Providers……………………………………………………………………………………………………… 14
Gartner Recommended Reading……………………………………………………………………………………………. 14
List of Tables
Table 1. Examples of Social Analytics Use Cases……………………………………………………………………….. 5
List of Figures
Figure 1. Customer-Related Social Analytics Inquiries by Industry…………………………………………………..3
What You Need to Know
While the number of vendors offering social analytics applications has somewhat stabilized, the
ability to look beyond the marketing messaging of the vendors to find out what the applications
specialize in has become more difficult. Gartner recommends that, before beginning any exploration
of vendors, IT application leaders should clarify the problem they are trying to solve or the
opportunity they want to pursue. This will help them identify the information and analytic capabilities
they need to make better, more informed decisions and narrow the range of vendors they need to
investigate.
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This research provides a technology overview of social analytics solutions focused on public-facing
social media and information on the selection criteria that can be used to differentiate the vendors
that offer solutions. The “Market Guide: Social Analytics for IT Application Leaders” goes into further
detail on specific vendors.
Analysis
Social analytics strategies and applications are the No. 1 inquiry topic among IT clients concerned
with public-facing social media as IT leaders attempt to extract value from, and quantify the impact
of, social media on their organization. In the last 12 months, inquiries have come from across
multiple industries as companies try to mature their social analytics deployments, primarily focusing
on understanding the customer (see Figure 1).
1
Figure 1. Customer-Related Social Analytics Inquiries by Industry
Source: Gartner (March 2015)
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When looking at public-facing social media, Facebook and Twitter are among the most popular
global social networks, but there is also significant activity in forums, review sites, blogs, and sites
hosting photos and videos. This research focuses on software applications that analyze publicfacing social media.
Technology Description
Social analytics is the process of collecting, measuring, analyzing and interpreting the results of
interactions and associations among people, topics and ideas. It is an umbrella term that includes a
number of specialized analysis techniques, such as social filtering, social network analysis, social
channel analysis, sentiment analysis and social media analytics. This technology overview examines
social analytics solutions that cover social filtering, sentiment analysis and public-facing social
media analysis. It focuses on solutions whose revenue is at least 60% software-based, including
software as a service (SaaS), for the purpose of this analysis, versus solutions that are mainly based
on consulting services.
Almost all social analytics applications in the market today are offered in a SaaS deployment model.
Uses
Companies use social analytics for a variety of different purposes (see Table 1).
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Table 1. Examples of Social Analytics Use Cases
Business
Objective
Use Cases
Role Using the Analysis
Risk Management
Compliance assurance of appropriateness of employee
speech in social media
Social media team, compliance
officers
Detection/identification of security risks and adversaries
Risk managers
Epidemiology: tracking illness through social media
content
Healthcare organizations,
government agencies
Personnel security: detecting overexposure of private
data
Security managers
Identify a crisis or breaking news event
Corporate communications
managers, marketing, brand or
product managers
Analyze activities of bad people who interact with each
other
Fraud risk managers
Customer service satisfaction, Customer experience
Customer service managers,
product managers
Anticipate product or service inquiries
Customer service managers
Route call to the correct agent based on caller
sentiment
Customer service managers
Identify accelerating negative sentiment and develop
scripts for dealing with negative trending topics
Customer service managers
Community health
Customer service managers
Brand reputation analysis, Brand health
Brand managers
Key executive reputation analysis
Corporate communication
managers, public relations
managers
Model customer behavior on a specific channel
Channel managers
Analyze a customer’s propensity to buy, based on
factors such as transactions, demographics or
psychographics combined with social interactions and
relationships
Marketing managers
Analyze the influence of a customer or group of
customers
Marketing or brand managers
Customer
Responsiveness
Reputation
Management
Marketing
Effectiveness
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Business
Objective
Channel
Effectiveness
Sales
Effectiveness
Use Cases
Role Using the Analysis
Predict buying behavior or channel preferences
Marketing managers
Lead tracking based on campaigns
Marketing or brand managers
Calculate reach awareness
Marketing or brand managers
Segment social conversations by topic
Marketing, brand or customer
service managers
Revenue/adoption rate of new products
Product managers
Adjust campaign tactics
Marketing managers
Measure and benchmark competitors’ sentiment,
influence, reach
Market researchers/competitive
intelligence
Explore trending topics on the social Web
Market researchers
Identify effective marketing channels and messages
Channel managers
Model customer behavior on a specific channel
Channel managers
Monitor and analyze process performance
Supply chain managers
Identify shortest path to key influencers
Sales managers
Identify key opinion leaders
Sales managers (pharmaceutical
industry)
Identify propensity to buy based on the social behaviors
of prospects
Sales managers and account reps
Source: Gartner (March 2015)
Benefits and Risks
Companies benefit from social analytics when they have a clear purpose and use for the analysis in
mind. For example, an Indian food manufacturer leveraged social analytics to identify their brand
and product shortcomings in order to make brand and product improvements. By making changes
2
and improvements based upon this insight, it was able to decrease negative sentiment by 42%.
Siloed instances of analyzing social media, such as comparing company sentiment to competitors,
can deliver business benefits, but maximum impact is achieved when social analytics are integrated
with other data sources and analytics processes to offer greater contextual analysis. Imagine, for
example, the Indian food manufacturer lined up sentiment with buying behavior analysis to calculate
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potential for actual churn based on dissatisfaction. That direct business impact metric is vastly
more valuable than simply knowing if sentiment has changed.
Siloed social analysis also brings additional risk. Techniques for accurately measuring sentiment of
unstructured data in particular are still evolving and, if clients use social data as the backbone of
their customer analytics strategy, they may be making a trade-off between time and effort versus
accuracy. Working with larger datasets can potentially improve the reliability of the sample on which
algorithms are run, or the ability to add human-coding of sentiment; however, this is more timeconsuming and costly.
Additionally, governance of social data the organization does not control affects the accuracy of
social analytics. Companies must have ways to identify the quality and integrity of social media —
for example, a competitor or the company itself manipulating social media to influence the customer
perception of the analytics and conclusions from social data analysis (see “How Crowdsourcing
Can Reduce the Reliability of Social Media Analytics”).
Technology Alternatives
There are two main alternatives to public-facing social media analytics solutions: social media
monitoring solutions and consulting services. Both alternatives assume end-user organizations have
recognized the needs for a more detailed or actionable solution beyond free Facebook Insights or
bit.ly link analysis, etc.
Social Media Monitoring
Social media monitoring solutions, often referred to as “listening” solutions, aggregate popular
social media feeds. These solutions present their consumers with activity metrics such as the
number of tweets coming in or the number of “likes” on a Facebook page, but fail to go deeper into
an analysis of the feed content or context.
Consulting Services
Consulting services can deliver social analytics and insight, usually through use of a social analytics
software application of their own. Consulting can save employees in-house from any IT or business
side work with a social analytics application, but is usually more expensive and reporting is less
frequent, as compared with when companies can do it themselves with an owned or subscribed
application.
Selection Guidelines
There are seven selection guidelines we recommend end users consider before settling on a publicfacing social media analytics solution:
1.
Information sources
2.
Approach to information analysis
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3.
Metrics captured
4.
Advanced analytics capabilities
5.
Reporting, dashboards and visualization
6.
Routing, notifications and alerts
7.
Scalability and architecture requirements
Emphasis on the specific criteria will vary based on use case and the size of deployment. Use “RFP
Toolkit: Social Analytics Applications for CRM” to put weightings on the criteria during the vendor
evaluation process, and to find out more about the details within the selection guidelines that follow.
1. Information Sources
Facebook and Twitter analyses are table stakes for social analytics vendors, but clients should also
consider owned sources and geographic or industry-specific sources, including social networks,
communities, reviews sites, forums and blogs:
■
■
■
■
Twitter:
■
@mentions
■
#hashtags
■
Keywords
■
Amount of the “fire hose” being analyzed
■
Latency and history
Facebook:
■
Owned pages
■
Unowned pages
■
Access to permissioned data
LinkedIn:
■
Company pages
■
Public profile data
■
User connections
Blogs
■
■
Blog comments
Communities
■
Owned
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■
Third-party managed
■
Forums
■
News outlets
■
Google+
■
Pinterest
■
YouTube
■
Descriptions and metadata
■
Comments
■
Instagram
■
Influencer rating networks:
■
Klout
■
Kred
■
APAC social networks, including The Renren Network, QQ and Mixi
■
LAM social networks
■
EMEA social networks, including Vkontakte, Viadeo and Xing
■
Open data such as Data.gov, Socrata, CIA World Factbook, AWS public datasets, Gapminder,
etc.
2. Information Analysis
The most basic social analytics solutions provide users with keyword analysis, simple Boolean
descriptors like “and,” “or,” and “without” and trend analyses based on keyword frequency. A
comprehensive list of analytics methods would include:
■
Machine/human ratio of analysis
■
Keyword analysis
■
Complexity of Boolean descriptors
■
Natural-language processing
■
Machine learning:
■
■
Supervised
■
Unsupervised
Sentiment analysis:
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■
■
■
Emotion analysis
■
Passion analysis
Topic analysis
■
Predefined
■
Discovered
Trend analysis:
■
Key terms
■
Key phrases
■
Acceleration/deceleration of trends
■
Cluster analysis
■
Influencer analysis
■
Affinity analysis
■
Geospatial and location analysis
■
Ability to self-adjust analysis requirements
3. Metrics
Gartner research “Choose Social Metrics that Demonstrate CRM Business Value” further addresses
social metrics, however, a sample list of metrics are as follows:
■
Number of questions asked
■
Average handling time
■
Number of questions answered
■
Time to first contact
■
Social media as a percentage of total question/inquiry volumes
■
Net sentiment
■
Clicked links
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Engagement rate
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Net Promoter score (NPS)
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Customer satisfaction
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Volume/mentions
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Size of fan base
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Gartner, Inc. | G00273268
This research note is restricted to the personal use of tdooling@oaktreecapital.com
This research note is restricted to the personal use of tdooling@oaktreecapital.com
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Retweets, shares, likes, etc.
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Reach
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Impressions
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Percentage share of voice
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Web traffic referrals
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Cost per impression
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Competitor volume
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Le
